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Lehrveranstaltungen
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Einführung in die Methoden der empirischen Sozialforschung Teil I -
- Dozentinnen/Dozenten:
- Michael Gebel, Christoph Schlee
- Angaben:
- Vorlesung, 2 SWS
- Termine:
- Di, 8:00 - 10:00, F21/01.37
- Voraussetzungen / Organisatorisches:
- Empfohlen für 1. Semester
Anmeldung:
Bitte tragen Sie sich bis zum 12.10.2023 in den VC der Veranstaltung ein: Link zum VC . Personen, die in den VC eingetragen sind, erhalten hierüber alle Informationen zur Lehrveranstaltung. Eine Anmeldung zur Vorlesung in FlexNow ist nicht nötig.
Informationen zur Prüfungsanmeldung werden im Rahmen der ersten Veranstaltung mitgeteilt.
Modulprüfung: Klausur (60 min.)
Barrierefreiheit: Wenden Sie sich im Vorfeld der Lehrveranstaltung bitte an den Dozenten, wenn Sie Bedarfe im Hinblick auf eine barrierefreie Teilnahme an der Lehrveranstaltung haben.
- Inhalt:
- Lernziel:
Im Anschluss an die Veranstaltung können die TeilnehmerInnen die zentralen Schritte des Forschungsprozesses benennen und die zu treffenden Entscheidungen erörtern, die Grundprinzipien theoriegeleiteter empirischer Forschung nachvollziehen und Hypothesen formulieren, Probleme der Konzeptspezifikation, Operationalsierung und Messung erläutern und anhand von Beispielen praktisch umsetzen, die grundlegende Idee und praktische Umsetzung verschiedener Auswahlverfahren erläutern, verschiedene Datenerhebungsmethoden erklären und deren Vor- und Nachteile kritisch miteinander vergleichen.
Lerninhalte:
Die Veranstaltung thematisiert Grundlagen der empirischen Sozialforschung in folgenden Themenfeldern:
Phasen und Ablauf des Forschungsprozesses
Richtlinien zur Generierung und Auswahl von Forschungsfragen
Theoriegeleitete empirische Forschung: Theorien, Formulierung von Hypothesen und ihre empirische Prüfung
Konzeptspezifikation und Operationalisierung
Messung: Gütekriterien, Indexbildung und Skalierungsverfahren
Stichprobenbeziehung und Auswahlverfahren
Die Befragung als Datenerhebungsverfahren und Grundlagen der Fragebogenkonstruktion
Alternative Datenerhebungstechniken: Beobachtung, Inhaltsanalyse und nicht-reaktive Verfahren
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Fortgeschrittene Verfahren der Längsschnittanalyse: Applied Panel Data Analysis -
- Dozentinnen/Dozenten:
- Michael Gebel, Chen-Hao Hsu
- Angaben:
- Seminar, 4 SWS
- Termine:
- Mo, 8:00 - 12:00, RZ/00.06
- Voraussetzungen / Organisatorisches:
- Requirements:
Students are expected to be familiar with the content of the compulsory lecture 'Research Design' and with multiple linear and binary logistic regression analysis. Students are also expected to be familiar with the statistical package Stata. These skills can be acquired or refreshed either through self-study or through an online tutorial. Link to Stata tutorial
Registration: Please register in the VC (Link to VC ) of the event until 12.10.2023. Only those registered in the VC will receive all information about the course.
Type of instruction:
On-site teaching
Language of instruction:
English
Module exam:
Portfolio in English (time: 3 months)
Barrier-free participation: Please contact the lecturer in advance of the course if you have any needs with regard to barrier-free participation in the course.
- Inhalt:
- Learning targets:
The central aim of this course is to enable participants to critically discuss the basic concepts and assumptions of various panel regression estimators for linear and binary dependent variables, to conduct theory-driven empirical research, to select and specify appropriate regression models according to the ideas of modern causal analysis, to independently carry out panel data analyses using the statistical package Stata and data from the German Socio-Economic Panel (SOEP), to correctly interpret the results and to clearly present the results of regression analyses in tables and graphs.
Course contents:
The course begins by explaining the basic logic of panel design compared to cross-sectional design, and by practicing the preparation and descriptive analysis of panel data in Stata. In the next step, pooled ordinary least squares (POLS), fixed effect (FE), first difference (FD), random effect (RE) and hybrid models for linear dependent variables are presented and explained. The different assumptions for the consistency of the estimators (=causal inference) are compared: the exogeneity assumptions are illustrated using causal graphs, and the implications of the full rank assumption for the inclusion of time-constant regressors and the need for sufficiently time-varying regressors are discussed. In addition, the assumptions for the efficiency of the estimators (= statistical inference) are presented, appropriate statistical tests (e.g. tests for serial correlation of error terms, Hausman test) are practised, and implications in the form of the use of panel-robust standard errors are explained. The different models are compared and practical recommendations for model selection are given. Based on the principles of theory-driven empirical research and modern causal analysis, the choice of time-constant and time-varying control variables is explained. In terms of model specification, the role of lagged dependent variables (LDV) and lagged explanatory variables (LEV) as control and causal variables (keywords: dynamic panel models, impact function, anticipation effects) is also discussed. Problems and possible solutions of bidirectional causality in the form of feedback loops and simultaneity are discussed. Aspects of modelling age, period and cohort effects and growth processes are discussed. Panel data models for binary dependent variables are presented. Finally, the issue of missing data in panel models is addressed.
In addition to theoretical introductions to the models, the logic of the various panel data estimators is illustrated using very simple game data. In laboratory sessions, participants will learn how to implement panel data analysis in practice using the statistical package Stata. The lab sessions and the seminar papers will be based exclusively on current sociological issues in life course research (consequences of life course events) and data from the German Socio-Economic Panel (SOEP). Specifically, the course offers an applied introduction and hands-on experience in the complex preparation of panel data for statistical analyses during the lab sessions.
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Research Design -
- Dozentinnen/Dozenten:
- Michael Gebel, Daniel Zeddel
- Angaben:
- Vorlesung, 2 SWS
- Termine:
- Di, 10:00 - 12:00, F21/02.55
- Voraussetzungen / Organisatorisches:
- Registration: Please register in the VC ( Link to VC ) of the event until 12.10.2023. Only those registered in the VC will receive all information about the course.
Language of instruction: English.
Module-related examination: Exam (time: 60 min) with instructions and questions in English. Answers can be given either in English or in German.
Barrier-free participation: Please contact the lecturer in advance of the course if you have any needs with regard to barrier-free participation in the course.
- Inhalt:
- Learning targets: Upon successful completion of the lecture, participants will be able to
- postulate research questions,
- derive and formulate hypotheses according to the principles of theory-driven empirical research
- explain and critically discuss advanced topics in causality and causal inference in experimental and nonexperimental cross-sectional and longitudinal research designs.
Course contents: Participants will learn to formulate research hypotheses and to distinguish between different types of causal hypotheses. They will reflect on how to derive hypotheses from theory according to the principles of theory-driven empirical social research. Rubin's notation of potential outcomes, which has become the backbone of modern causal analysis in the social sciences, will be introduced. Directed Acyclic Graphs (DAGs) will also be introduced, as they provide an illustrative graphical approach to the problem of causal inference. Advanced issues of common cause confounding, overcontrol bias and endogenous selection bias will be discussed in the context of DAGs. Advanced issues of validity, in particular causal inference, will be addressed in experimental and nonexperimental cross-sectional and longitudinal research designs. The course is divided into input units and supplementary practical exercise units. Throughout the course, practical examples from empirical sociological research will be critically discussed.
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